Models & Labs
OpenAI Develops Custom Jalapeño Chip with Broadcom
TechCrunch AIhigh confidence
Why it matters
- →Custom chips can provide performance optimizations tailored to specific AI workloads.
- →Reducing dependency on Nvidia could lead to more competitive pricing and innovation in AI hardware.
- →OpenAI's move reflects a broader industry trend towards custom silicon for strategic control.
OpenAI has announced its plans to develop a custom inference chip called Jalapeño in collaboration with Broadcom. This move is part of a broader trend among tech giants to reduce dependency on Nvidia's GPUs by creating hardware tailored to specific needs. The Jalapeño chip is expected to provide OpenAI with more control and potentially significant performance improvements. This development places OpenAI alongside companies like Google and Apple, who are also pursuing custom silicon solutions.
Read originalMore from TechCrunch AI
© TechCrunch AIMarket & Regulationbusiness
OpenAI acquires Glass Imaging for $300M
OpenAI's acquisition of Glass Imaging for over $300 million signals a strategic move into hardware, leveraging AI to enhance smartphone camera capabilities. Glass Imaging, founded by former Apple engineers, specializes in using neural networks to improve image quality at the moment of capture, rather than post-processing. This acquisition aligns with rumors of OpenAI's interest in developing its own hardware, potentially including smartphones and AI companion devices. The move could position OpenAI to integrate advanced AI-driven imaging technology into future products, expanding its influence beyond software.
TechCrunch AI
© TechCrunch AIMore in Models & Labs
Models & Labsmodels
llama.cpp b10955 release addresses heap corruption
The latest release of llama.cpp, b10955, tackles a critical issue of heap corruption by disabling the ggml-cpu precompiled header and fixing CACHE_LINE_SIZE ambiguity. This update ensures consistent CACHE_LINE_SIZE values across C++ kernels and C work-buffer sizing code, preventing heap-buffer-overflow and subsequent crashes. By restoring the natural include order and removing the std::hardware_destructive_interference_size branch, the update makes the value deterministic and include-order independent. This release is a technical fix that stabilizes the runtime environment for developers using llama.cpp.
llama.cpp Releases
Models & Labsmodels
llama.cpp b10956 release enhances SYCL backend
The latest llama.cpp release, b10956, introduces significant improvements to the SYCL backend, particularly for handling large k values in TOP_K operations. By implementing a radix select method, the update allows for efficient GPU-resident processing, avoiding previous limitations that forced operations to fall back to the CPU. This change enhances performance, especially in scenarios requiring large k values, such as qwen4exp's sparse-attention indexer. The update ensures that operations are more efficient and scalable, providing a notable boost in processing speed without regressing any measured shapes.
